Ligue de hockey Simulée Ste-Flanelle

Kansas City
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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Joueur	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Âge	Contrat	Salaire Moyen
1	Nico Sturm (R)	X				100.00	81	77	89	74	77	67	69	61	76	59	58	67	55	44	44	0	50	61	0	24	3	925,000$
2	Joachim Blichfeld (R)			X		100.00	73	69	83	66	69	72	75	64	50	59	65	64	62	44	44	0	50	60	0	21	3	736,666$
3	John Quenneville	X	X	X		100.00	74	66	79	73	72	61	60	61	78	51	61	67	62	47	47	0	50	59	0	23	1	560,000$
4	Kieffer Bellows		X			100.00	72	71	74	69	71	77	82	59	50	47	67	62	64	44	44	0	50	59	0	21	2	925,000$
5	Luke Philp (R)	X				100.00	72	65	88	67	65	60	60	60	75	51	65	62	62	44	44	0	50	58	0	23	1	560,000$
6	Adam Mascherin		X			100.00	75	73	80	61	73	66	70	53	50	51	51	62	48	44	44	0	50	55	0	21	2	650,000$
7	Tanner Laczynski (R)	X				100.00	56	52	82	67	74	63	76	55	64	54	46	51	48	60	60	0	50	55	0	22	3	925,000$
8	Jonathan Dugan (R)		X			100.00	61	56	59	67	71	61	74	53	62	52	44	54	46	58	58	0	50	54	0	21	3	525,000$
9	Scott Reedy (R)	X				100.00	54	51	89	66	75	61	75	51	56	37	55	49	58	54	54	0	50	53	0	20	1	560,000$
10	Maxim Shalunov (R)			X		100.00	32	40	40	40	60	31	31	32	40	32	32	40	36	32	30	0	50	36	0	26	1	560,000$
11	David Cotton (R)	X				100.00	32	37	37	37	71	31	31	32	37	32	32	37	34	32	30	0	50	35	0	22	1	525,000$
12	Jordan Oesterle (C)				X	100.00	72	42	92	77	67	73	82	63	25	52	47	69	25	61	61	0	50	64	0	27	1	2,200,000$
13	Brogan Rafferty (R)				X	100.00	78	72	92	64	72	64	66	60	25	60	45	65	43	44	44	0	50	60	0	24	1	650,000$
14	Michael Anderson (R)				X	100.00	76	71	89	77	71	63	67	49	25	41	41	61	39	44	44	0	50	58	0	20	3	925,000$
15	Leon Gawanke (R)				X	100.00	76	72	86	63	72	68	72	54	25	52	41	63	39	44	44	0	50	58	0	20	4	796,667$
16	Jeremy Davies (R)				X	100.00	70	66	79	68	66	64	67	52	25	49	41	59	39	44	44	0	50	56	0	22	3	925,000$
17	Dmitri Samorukov (R)				X	100.00	77	74	85	62	74	65	70	46	25	37	40	61	38	44	44	0	50	56	0	20	3	825,000$
Rayé
1	Artyom Zub (R)				X	100.00	45	45	45	45	45	45	45	45	45	45	45	45	45	45	45	0	1	43	0	24	4	925,000$
2	Jack Ahcan (R)				X	100.00	41	45	45	45	55	39	39	41	45	41	41	45	43	32	30	0	50	41	0	22	2	825,000$

	MOYENNE D'ÉQUIPE					100.00	64	60	74	63	68	60	64	52	46	47	48	57	47	45	45	0	47	54

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Kirill Ustimenko (R)	100.00	59	71	66	70	55	59	59	59	57	58	58	54	54	0	50	59	0
2	Kevin Lankinen (R)	100.00	45	45	45	45	45	45	45	45	45	45	45	45	45	0	1	44	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	52	58	56	58	50	52	52	52	51	52	52	50	50	0	26	52

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FL	FT	S1	S2	S3
1	Kieffer Bellows	Jayhawks (KC )	LW	70	56	30	86	41	30	0	161	139	421	105	283	13.30%	18	1193	17.05	7	7	14	28	57	0	0	3	40	8	1	49.18%	61	5	1.44	0	1	0	0	14	5	2
2	Nico Sturm	Jayhawks (KC )	C	74	36	34	70	23	50	10	114	228	294	76	195	12.24%	24	1091	14.74	5	6	11	30	87	0	0	0	0	7	0	60.90%	1491	1	1.28	1	1	1	0	4	5	2
3	Brogan Rafferty	Jayhawks (KC )	D	68	11	56	67	33	42	0	189	107	140	53	111	7.86%	142	1573	23.14	7	11	18	79	168	2	2	0	151	3	1	0.00%	0	0	0.85	0	0	0	0	4	0	5
4	Luke Philp	Jayhawks (KC )	C	49	30	29	59	21	10	0	40	148	202	55	153	14.85%	13	818	16.70	4	6	10	20	67	0	0	0	0	5	1	55.47%	1170	1	1.44	0	0	0	0	4	0	1
5	Joachim Blichfeld	Jayhawks (KC )	RW	78	27	29	56	7	24	0	142	147	343	102	238	7.87%	23	1237	15.87	2	7	9	38	90	0	0	0	0	4	1	47.42%	97	0	0.90	0	0	0	0	2	3	2
6	Michael Anderson	Jayhawks (KC )	D	78	8	39	47	2	76	10	217	92	145	51	100	5.52%	141	1541	19.77	4	10	14	63	139	0	0	0	112	0	0	0.00%	0	0	0.61	0	0	1	1	0	1	2
7	Jordan Oesterle	Jayhawks (KC )	D	46	4	32	36	-2	14	0	75	73	81	31	57	4.94%	72	790	17.18	0	2	2	5	12	0	0	0	1	0	0	0.00%	0	0	0.91	0	0	0	0	1	1	2
8	Leon Gawanke	Jayhawks (KC )	D	61	7	29	36	19	50	10	151	70	55	22	36	12.73%	96	1078	17.68	0	0	0	0	0	0	0	0	0	0	2	0.00%	0	0	0.67	0	0	1	1	0	2	3
9	John Quenneville	Jayhawks (KC )	C/LW/RW	55	19	15	34	12	20	0	62	70	149	47	102	12.75%	15	540	9.82	0	0	0	1	0	0	0	0	0	2	1	60.81%	273	0	1.26	0	1	0	0	1	2	2
10	Adam Mascherin	Jayhawks (KC )	LW	53	8	18	26	16	28	10	67	37	102	25	71	7.84%	15	637	12.02	0	0	0	1	2	0	0	1	49	0	0	41.30%	46	0	0.82	0	0	1	1	2	0	1
11	Tanner Laczynski	Jayhawks (KC )	C	51	3	19	22	14	6	0	13	37	81	22	49	3.70%	7	612	12.01	0	0	0	0	0	0	0	0	0	0	0	58.55%	193	0	0.72	0	0	0	0	0	1	0
12	Jonathan Dugan	Jayhawks (KC )	LW	43	3	10	13	3	12	0	69	45	64	25	57	4.69%	6	509	11.84	0	0	0	0	0	0	0	0	0	1	1	46.88%	32	0	0.51	0	0	0	0	0	1	1
13	Dmitri Samorukov	Jayhawks (KC )	D	26	2	7	9	-3	16	0	54	11	16	3	5	12.50%	24	394	15.18	0	0	0	0	0	0	0	1	5	1	0	0.00%	0	0	0.46	0	0	0	0	0	0	2
14	Scott Reedy	Jayhawks (KC )	C	28	5	3	8	3	2	0	15	23	38	9	31	13.16%	4	290	10.37	0	0	0	0	0	0	0	0	0	0	0	50.00%	130	0	0.55	0	0	0	0	0	0	0
15	Jeremy Davies	Jayhawks (KC )	D	26	1	4	5	2	12	0	22	18	34	2	20	2.94%	19	289	11.13	0	0	0	0	0	0	0	0	0	0	0	25.00%	8	0	0.35	0	0	0	0	0	0	1
16	Maxim Shalunov	Jayhawks (KC )	RW	4	1	0	1	2	0	0	1	1	2	0	0	50.00%	0	65	16.47	0	0	0	0	0	0	0	0	0	0	0	25.00%	4	0	0.30	0	0	0	0	0	0	0
17	David Cotton	Jayhawks (KC )	C	12	1	0	1	0	0	0	0	13	2	0	0	50.00%	1	96	8.05	0	0	0	0	0	0	0	0	0	0	0	41.90%	105	0	0.21	0	0	0	0	0	0	0
18	Stephen Johns	Kansas City	D	1	0	0	0	-1	0	0	7	0	0	2	1	0.00%	1	23	23.35	0	0	0	0	2	0	0	0	3	0	0	0.00%	0	0	0.00	0	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				823	222	354	576	192	392	40	1399	1259	2169	630	1509	10.24%	621	12783	15.53	29	49	78	265	628	2	2	5	364	31	8	57.01%	3610	7	0.90	1	3	4	3	32	21	26

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3
1	Kirill Ustimenko	Jayhawks (KC )	78	42	19	13	0.922	3.22	4543	19	1	244	3145	0	2	2	0.676	37	78	0	11	7	4
Stats d'équipe Total ou en Moyenne			78	42	19	13	0.922	3.22	4543	19	1	244	3145	0	2	2	0.676	37	78	0	11	7	4

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Salaire Année 4	Link
Adam Mascherin	Jayhawks (KC )	LW	21	1998-06-05	No	206 Lbs	5 ft11	No	No	No	2	Pro & Farm	650,000$	24,462$	65,000$	2,446$	No	650,000$			Lien
Artyom Zub	Jayhawks (KC )	D	24	1995-10-03 17:43:45	Yes	198 Lbs	6 ft2	No	No	No	4	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$	925,000$	925,000$
Brogan Rafferty	Jayhawks (KC )	D	24	1995-05-28	Yes	192 Lbs	6 ft2	No	No	No	1	Pro & Farm	650,000$	24,462$	65,000$	2,446$	No				Lien
David Cotton	Jayhawks (KC )	C	22	1997-07-09	Yes	204 Lbs	6 ft3	No	No	No	1	Pro & Farm	525,000$	19,758$	52,500$	1,976$	No				Lien
Dmitri Samorukov	Jayhawks (KC )	D	20	1999-06-16	Yes	196 Lbs	6 ft3	No	No	No	3	Pro & Farm	825,000$	31,048$	82,500$	3,105$	No	825,000$	825,000$		Lien
Jack Ahcan	Jayhawks (KC )	D	22	1997-05-18	Yes	184 Lbs	5 ft8	No	No	No	2	Pro & Farm	825,000$	31,048$	82,500$	3,105$	No	825,000$			Lien
Jeremy Davies	Jayhawks (KC )	D	22	1996-12-04	Yes	181 Lbs	5 ft11	No	No	No	3	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$	925,000$		Lien
Joachim Blichfeld	Jayhawks (KC )	RW	21	1998-07-16	Yes	180 Lbs	6 ft2	No	No	No	3	Pro & Farm	736,666$	27,723$	73,667$	2,772$	No	736,666$	736,666$		Lien
John Quenneville	Jayhawks (KC )	C/LW/RW	23	1996-04-16	No	195 Lbs	6 ft1	Yes	No	No	1	Pro & Farm	560,000$	21,075$	56,000$	2,108$	No				Lien
Jonathan Dugan	Jayhawks (KC )	LW	21	1998-03-24	Yes	192 Lbs	6 ft2	No	No	No	3	Pro & Farm	525,000$	19,758$	52,500$	1,976$	No	525,000$	525,000$		Lien
Jordan Oesterle	Jayhawks (KC )	D	27	1992-06-24	No	182 Lbs	6 ft0	Yes	No	No	1	Pro & Farm	2,200,000$	82,795$	220,000$	8,280$	No				Lien
Kevin Lankinen	Jayhawks (KC )	G	24	1995-04-28 17:36:52	Yes	185 Lbs	6 ft2	No	No	No	4	Pro & Farm	800,000$	30,107$	80,000$	3,011$	No	800,000$	800,000$	800,000$
Kieffer Bellows	Jayhawks (KC )	LW	21	1998-06-10	No	196 Lbs	6 ft0	No	No	No	2	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$			Lien
Kirill Ustimenko	Jayhawks (KC )	G	20	1999-01-29	Yes	187 Lbs	6 ft3	No	No	No	3	Pro & Farm	825,833$	31,079$	82,583$	3,108$	No	825,833$	825,833$		Lien
Leon Gawanke	Jayhawks (KC )	D	20	1999-05-31	Yes	198 Lbs	6 ft1	No	No	No	4	Pro & Farm	796,667$	29,982$	79,667$	2,998$	No	796,667$	796,667$	796,667$
Luke Philp	Jayhawks (KC )	C	23	1995-11-05	Yes	181 Lbs	5 ft10	Yes	No	No	1	Pro & Farm	560,000$	21,075$	56,000$	2,108$	No				Lien
Maxim Shalunov	Jayhawks (KC )	RW	26	1993-01-31	Yes	185 Lbs	6 ft3	Yes	No	No	1	Pro & Farm	560,000$	21,075$	56,000$	2,108$	No				Lien
Michael Anderson	Jayhawks (KC )	D	20	1999-05-25	Yes	196 Lbs	6 ft0	No	No	No	3	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$	925,000$		Lien
Nico Sturm	Jayhawks (KC )	C	24	1995-05-02	Yes	207 Lbs	6 ft3	No	No	No	3	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$	925,000$		Lien
Scott Reedy	Jayhawks (KC )	C	20	1999-04-04	Yes	205 Lbs	6 ft2	Yes	No	No	1	Pro & Farm	560,000$	21,075$	56,000$	2,108$	No				Lien
Tanner Laczynski	Jayhawks (KC )	C	22	1997-06-01	Yes	205 Lbs	6 ft1	No	No	No	3	Pro & Farm	925,000$	34,811$	92,500$	3,481$	No	925,000$	925,000$		Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
21	22.24	193 Lbs	6 ft1	2.33	816,627$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Kieffer Bellows	Nico Sturm	Maxim Shalunov	40	1	2	2
2	Adam Mascherin	Luke Philp	Joachim Blichfeld	30	1	2	2
3	Jonathan Dugan	David Cotton	Scott Reedy	20	1	2	2
4	Jonathan Dugan	Scott Reedy	John Quenneville	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jordan Oesterle	Brogan Rafferty	40	1	2	2
2	Dmitri Samorukov	Michael Anderson	30	1	2	2
3	Jeremy Davies	Leon Gawanke	20	1	2	2
4	Leon Gawanke		10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1				60	1	2	2
2		Nico Sturm	Joachim Blichfeld	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2		Michael Anderson	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1			60	1	2	2
2		Michael Anderson	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		60	1	2	2			60	1	2	2
2		40	1	2	2		Michael Anderson	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Leon Gawanke		60	1	2	2
2		Michael Anderson	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,	,

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
, ,		,

Tirs de Pénalité
, , , ,

Gardien
#1 : Kirill Ustimenko, #2 :

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Admirals	4	2	0	0	0	1	0	1	21	18	3	2	1	0	0	0	1	0	0	10	8	2	2	1	0	0	0	0	0	1	11	10	1	6	0.750	21	38	59	0	0	130	105	65	7	152	1056	1029	1033	74	162	46	26	115	15	7	46.67%	13	3	76.92%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
2	Baby Hawks	3	2	1	0	0	0	0	0	9	7	2	2	2	0	0	0	0	0	0	7	4	3	1	0	1	0	0	0	0	0	2	3	-1	4	0.667	9	15	24	0	0	130	105	65	7	114	1056	1029	1033	74	107	37	29	76	13	2	15.38%	11	2	81.82%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
3	Bears	2	1	1	0	0	0	0	0	7	9	-2	1	1	0	0	0	0	0	0	3	2	1	1	0	1	0	0	0	0	0	4	7	-3	2	0.500	7	12	19	0	0	130	105	65	7	74	1056	1029	1033	74	76	29	14	52	9	2	22.22%	6	4	33.33%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
4	Bruins	2	0	1	0	0	0	0	1	4	7	-3	1	0	1	0	0	0	0	0	2	4	-2	1	0	0	0	0	0	0	1	2	3	-1	1	0.250	4	8	12	0	0	130	105	65	7	77	1056	1029	1033	74	96	35	21	56	5	0	0.00%	8	2	75.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
5	Cabaret Lady Mary Ann	2	2	0	0	0	0	0	0	11	3	8	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	6	1	5	4	1.000	11	20	31	0	0	130	105	65	7	136	1056	1029	1033	74	94	29	8	70	6	1	16.67%	4	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
6	Caroline	2	2	0	0	0	0	0	0	9	6	3	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	5	4	1	4	1.000	9	17	26	0	0	130	105	65	7	56	1056	1029	1033	74	76	19	6	38	1	0	0.00%	3	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
7	Chiefs	3	1	1	0	0	1	0	0	9	10	-1	1	0	1	0	0	0	0	0	2	4	-2	2	1	0	0	0	1	0	0	7	6	1	3	0.500	9	15	24	0	0	130	105	65	7	85	1056	1029	1033	74	165	44	34	83	7	1	14.29%	15	5	66.67%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
8	Chill	3	2	0	0	0	0	0	1	13	10	3	2	1	0	0	0	0	0	1	8	6	2	1	1	0	0	0	0	0	0	5	4	1	5	0.833	13	23	36	0	0	130	105	65	7	90	1056	1029	1033	74	132	32	16	69	8	2	25.00%	8	2	75.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
9	Comets	3	1	2	0	0	0	0	0	9	14	-5	1	0	1	0	0	0	0	0	2	4	-2	2	1	1	0	0	0	0	0	7	10	-3	2	0.333	9	16	25	0	0	130	105	65	7	105	1056	1029	1033	74	126	29	20	51	8	1	12.50%	10	4	60.00%	1	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
10	Cougars	2	2	0	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	1	2	4	1.000	6	11	17	0	0	130	105	65	7	76	1056	1029	1033	74	77	22	20	46	7	0	0.00%	10	1	90.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
11	Crunch	2	1	0	0	0	0	0	1	6	6	0	1	0	0	0	0	0	0	1	4	5	-1	1	1	0	0	0	0	0	0	2	1	1	3	0.750	6	8	14	0	0	130	105	65	7	72	1056	1029	1033	74	84	34	12	54	4	0	0.00%	6	2	66.67%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
12	Heat	4	4	0	0	0	0	0	0	22	13	9	2	2	0	0	0	0	0	0	10	4	6	2	2	0	0	0	0	0	0	12	9	3	8	1.000	22	29	51	0	0	130	105	65	7	190	1056	1029	1033	74	156	50	16	100	12	1	8.33%	8	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
13	Las Vegas	5	2	3	0	0	0	0	0	11	16	-5	2	1	1	0	0	0	0	0	4	5	-1	3	1	2	0	0	0	0	0	7	11	-4	4	0.400	11	19	30	0	0	130	105	65	7	158	1056	1029	1033	74	215	61	60	113	17	2	11.76%	20	3	85.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
14	Manchots	2	2	0	0	0	0	0	0	13	2	11	1	1	0	0	0	0	0	0	5	0	5	1	1	0	0	0	0	0	0	8	2	6	4	1.000	13	25	38	0	1	130	105	65	7	94	1056	1029	1033	74	76	20	8	60	7	4	57.14%	3	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
15	Marlies	2	0	1	0	0	0	0	1	8	12	-4	1	0	0	0	0	0	0	1	5	6	-1	1	0	1	0	0	0	0	0	3	6	-3	1	0.250	8	16	24	0	0	130	105	65	7	90	1056	1029	1033	74	97	30	12	44	8	3	37.50%	5	2	60.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
16	Minnesota	3	2	0	0	0	1	0	0	18	9	9	2	1	0	0	0	1	0	0	13	6	7	1	1	0	0	0	0	0	0	5	3	2	5	0.833	18	30	48	0	0	130	105	65	7	224	1056	1029	1033	74	108	41	10	69	2	0	0.00%	5	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
17	Monarchs	4	1	1	0	0	1	0	1	16	18	-2	2	0	1	0	0	0	0	1	8	11	-3	2	1	0	0	0	1	0	0	8	7	1	4	0.500	16	29	45	0	0	130	105	65	7	165	1056	1029	1033	74	219	69	46	112	12	4	33.33%	20	7	65.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
18	Monsters	2	2	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	4	2	2	4	1.000	8	15	23	0	0	130	105	65	7	94	1056	1029	1033	74	65	22	10	40	10	1	10.00%	5	1	80.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
19	Monsters	2	1	0	0	0	0	0	1	10	8	2	1	1	0	0	0	0	0	0	8	5	3	1	0	0	0	0	0	0	1	2	3	-1	3	0.750	10	20	30	0	0	130	105	65	7	66	1056	1029	1033	74	88	27	14	47	3	1	33.33%	7	4	42.86%	1	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
20	Oceanics	2	0	1	0	0	1	0	0	5	8	-3	0	0	0	0	0	0	0	0	0	0	0	2	0	1	0	0	1	0	0	5	8	-3	1	0.250	5	8	13	0	0	130	105	65	7	67	1056	1029	1033	74	102	30	6	56	5	0	0.00%	3	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
21	Oil Kings	4	0	3	0	0	1	0	0	11	17	-6	2	0	1	0	0	1	0	0	6	9	-3	2	0	2	0	0	0	0	0	5	8	-3	1	0.125	11	21	32	1	0	130	105	65	7	144	1056	1029	1033	74	167	41	24	121	11	2	18.18%	10	3	70.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
22	Phantoms	2	0	2	0	0	0	0	0	5	9	-4	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	3	5	-2	0	0.000	5	9	14	1	0	130	105	65	7	75	1056	1029	1033	74	88	19	14	56	5	0	0.00%	7	1	85.71%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
23	Rocket	2	1	1	0	0	0	0	0	7	8	-1	1	1	0	0	0	0	0	0	5	4	1	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	7	13	20	0	0	130	105	65	7	75	1056	1029	1033	74	104	21	18	46	3	0	0.00%	9	3	66.67%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
24	Senators	2	2	0	0	0	0	0	0	9	4	5	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	5	1	4	4	1.000	9	17	26	0	0	130	105	65	7	89	1056	1029	1033	74	79	21	8	40	8	3	37.50%	4	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
25	Sharks	3	1	0	0	0	0	1	1	13	11	2	2	1	0	0	0	0	0	1	10	9	1	1	0	0	0	0	0	1	0	3	2	1	5	0.833	13	23	36	0	0	130	105	65	7	104	1056	1029	1033	74	108	31	23	84	10	6	60.00%	9	3	66.67%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
26	Sound Tigers	2	2	0	0	0	0	0	0	10	5	5	1	1	0	0	0	0	0	0	5	4	1	1	1	0	0	0	0	0	0	5	1	4	4	1.000	10	18	28	0	0	130	105	65	7	90	1056	1029	1033	74	84	23	13	50	8	1	12.50%	4	1	75.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
27	Spiders	2	1	1	0	0	0	0	0	8	11	-3	1	1	0	0	0	0	0	0	6	4	2	1	0	1	0	0	0	0	0	2	7	-5	2	0.500	8	14	22	0	0	130	105	65	7	83	1056	1029	1033	74	89	25	14	43	11	3	27.27%	7	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
28	Stars	3	2	1	0	0	0	0	0	9	8	1	2	1	1	0	0	0	0	0	6	6	0	1	1	0	0	0	0	0	0	3	2	1	4	0.667	9	18	27	0	0	130	105	65	7	138	1056	1029	1033	74	106	19	14	71	7	1	14.29%	5	1	80.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
29	Thunder	2	1	0	0	0	0	1	0	5	3	2	1	0	0	0	0	0	1	0	3	2	1	1	1	0	0	0	0	0	0	2	1	1	4	1.000	5	5	10	0	0	130	105	65	7	80	1056	1029	1033	74	63	22	17	46	4	0	0.00%	5	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
	Total	78	42	20	0	0	6	2	8	302	264	38	39	22	8	0	0	3	1	5	158	128	30	39	20	12	0	0	3	1	3	144	136	8	102	0.654	302	532	834	2	1	130	105	65	7	3152	1056	1029	1033	74	3274	944	542	1959	229	48	20.96%	232	54	76.72%	2	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
30	Wolf Pack	2	2	0	0	0	0	0	0	10	5	5	1	1	0	0	0	0	0	0	4	1	3	1	1	0	0	0	0	0	0	6	4	2	4	1.000	10	20	30	0	0	130	105	65	7	89	1056	1029	1033	74	65	16	9	51	3	0	0.00%	2	0	100.00%	0	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
	_Since Last GM Reset	78	42	20	0	0	6	2	8	302	264	38	39	22	8	0	0	3	1	5	158	128	30	39	20	12	0	0	3	1	3	144	136	8	102	0.654	302	532	834	2	1	130	105	65	7	3152	1056	1029	1033	74	3274	944	542	1959	229	48	20.96%	232	54	76.72%	2	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501
	_Vs Conference	40	23	12	0	0	3	0	2	147	128	19	20	12	5	0	0	2	0	1	79	62	17	20	11	7	0	0	1	0	1	68	66	2	51	0.638	147	252	399	1	0	130	105	65	7	1639	1056	1029	1033	74	1673	474	285	985	101	12	11.88%	123	28	77.24%	2	1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%	1849	1291	1900	560	998	501

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
78	102	SOL1	302	532	834	3152	3274	944	542	1959	2	1

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
78	42	20	0	6	2	8	302	264

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
39	22	8	0	3	1	5	158	128

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
39	20	12	0	3	1	3	144	136

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
5	4	0	0	0	1

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
229	48	20.96%	232	54	76.72%	2

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
1056	1029	1033	74	130	105	65	7

Mises en Jeu
Gagnées en Zone Offensive	Total en Zone Offensive	% Gagnées en Zone Offensive	Gagnées en Zone Défensive	Total en Zone Défensive	% Gagnées en Zone Défensive	Gagnées en Zone Neutre	Total en Zone Neutre	% Gagnées en Zone Neutre
1376	2844	48.38%	1335	3013	44.31%	625	1363	45.85%

Temps Avec la Rondelle
En Zone Offensive	Contrôle en Zone Offensive	En Zone Défensive	Contrôle en Zone Défensive	En Zone Neutre	Contrôle en Zone Neutre
1849	1291	1900	560	998	501

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
2 - 2020-10-23	12	Jayhawks	5	Admirals	6	L	X	X	Sommaire du Match
4 - 2020-10-25	26	Bruins	4	Jayhawks	2	L			Sommaire du Match
9 - 2020-10-30	57	Las Vegas	4	Jayhawks	2	L			Sommaire du Match
11 - 2020-11-01	72	Jayhawks	2	Monsters	3	L	X	X	Sommaire du Match
14 - 2020-11-04	87	Jayhawks	4	Oceanics	5	L	X		Sommaire du Match
16 - 2020-11-06	104	Chill	2	Jayhawks	5	W			Sommaire du Match
18 - 2020-11-08	115	Senators	3	Jayhawks	4	W			Sommaire du Match
21 - 2020-11-11	136	Jayhawks	6	Wolf Pack	4	W			Sommaire du Match
23 - 2020-11-13	146	Jayhawks	5	Sound Tigers	1	W			Sommaire du Match
24 - 2020-11-14	157	Jayhawks	2	Spiders	7	L			Sommaire du Match
27 - 2020-11-17	177	Jayhawks	2	Crunch	1	W			Sommaire du Match
29 - 2020-11-19	191	Rocket	4	Jayhawks	5	W			Sommaire du Match
32 - 2020-11-22	214	Monsters	5	Jayhawks	8	W			Sommaire du Match
34 - 2020-11-24	223	Jayhawks	2	Oil Kings	3	L			Sommaire du Match
35 - 2020-11-25	231	Jayhawks	6	Heat	4	W			Sommaire du Match
37 - 2020-11-27	246	Monsters	2	Jayhawks	4	W			Sommaire du Match
39 - 2020-11-29	259	Minnesota	5	Jayhawks	4	L	X		Sommaire du Match
41 - 2020-12-01	270	Jayhawks	4	Bears	7	L			Sommaire du Match
42 - 2020-12-02	275	Jayhawks	4	Chiefs	2	W			Sommaire du Match
44 - 2020-12-04	289	Jayhawks	5	Minnesota	3	W			Sommaire du Match
46 - 2020-12-06	301	Heat	2	Jayhawks	4	W			Sommaire du Match
48 - 2020-12-08	317	Monarchs	4	Jayhawks	3	L	X	X	Sommaire du Match
51 - 2020-12-11	343	Marlies	6	Jayhawks	5	L	X	X	Sommaire du Match
53 - 2020-12-13	350	Jayhawks	4	Monarchs	5	L	X		Sommaire du Match
54 - 2020-12-14	364	Oil Kings	5	Jayhawks	3	L			Sommaire du Match
57 - 2020-12-17	386	Admirals	7	Jayhawks	6	L	X		Sommaire du Match
59 - 2020-12-19	399	Jayhawks	1	Las Vegas	6	L			Sommaire du Match
60 - 2020-12-20	411	Sharks	5	Jayhawks	7	W			Sommaire du Match
63 - 2020-12-23	429	Jayhawks	4	Monsters	2	W			Sommaire du Match
65 - 2020-12-25	442	Jayhawks	3	Phantoms	5	L			Sommaire du Match
66 - 2020-12-26	449	Jayhawks	8	Manchots	2	W			Sommaire du Match
68 - 2020-12-28	466	Jayhawks	2	Baby Hawks	3	L			Sommaire du Match
70 - 2020-12-30	480	Heat	2	Jayhawks	6	W			Sommaire du Match
72 - 2021-01-01	495	Baby Hawks	1	Jayhawks	3	W			Sommaire du Match
74 - 2021-01-03	512	Spiders	4	Jayhawks	6	W			Sommaire du Match
77 - 2021-01-06	534	Jayhawks	3	Sharks	2	W	X	X	Sommaire du Match
79 - 2021-01-08	546	Minnesota	1	Jayhawks	9	W			Sommaire du Match
82 - 2021-01-11	567	Jayhawks	3	Cougars	1	W			Sommaire du Match
83 - 2021-01-12	576	Jayhawks	5	Chill	4	W			Sommaire du Match
88 - 2021-01-17	600	Jayhawks	5	Las Vegas	2	W			Sommaire du Match
89 - 2021-01-18	609	Stars	2	Jayhawks	3	W			Sommaire du Match
91 - 2021-01-20	625	Chiefs	4	Jayhawks	2	L			Sommaire du Match
93 - 2021-01-22	636	Admirals	1	Jayhawks	4	W			Sommaire du Match
95 - 2021-01-24	650	Phantoms	4	Jayhawks	2	L			Sommaire du Match
98 - 2021-01-27	664	Jayhawks	6	Cabaret Lady Mary Ann	1	W			Sommaire du Match
100 - 2021-01-29	680	Jayhawks	2	Thunder	1	W			Sommaire du Match
101 - 2021-01-30	690	Jayhawks	5	Caroline	4	W			Sommaire du Match
103 - 2021-02-01	706	Manchots	0	Jayhawks	5	W			Sommaire du Match
105 - 2021-02-03	724	Sharks	4	Jayhawks	3	L	X	X	Sommaire du Match
107 - 2021-02-05	739	Jayhawks	4	Comets	3	W			Sommaire du Match
109 - 2021-02-07	745	Jayhawks	3	Oil Kings	5	L			Sommaire du Match
120 - 2021-02-18	778	Jayhawks	6	Admirals	4	W			Sommaire du Match
121 - 2021-02-19	784	Monarchs	7	Jayhawks	5	L			Sommaire du Match
123 - 2021-02-21	800	Baby Hawks	3	Jayhawks	4	W			Sommaire du Match
126 - 2021-02-24	824	Oil Kings	4	Jayhawks	3	L	X		Sommaire du Match
128 - 2021-02-26	838	Caroline	2	Jayhawks	4	W			Sommaire du Match
130 - 2021-02-28	844	Jayhawks	2	Bruins	3	L	X	X	Sommaire du Match
132 - 2021-03-02	860	Jayhawks	2	Rocket	4	L			Sommaire du Match
133 - 2021-03-03	867	Jayhawks	3	Marlies	6	L			Sommaire du Match
135 - 2021-03-05	885	Jayhawks	5	Senators	1	W			Sommaire du Match
137 - 2021-03-07	904	Bears	2	Jayhawks	3	W			Sommaire du Match
139 - 2021-03-09	917	Sound Tigers	4	Jayhawks	5	W			Sommaire du Match
141 - 2021-03-11	929	Jayhawks	3	Stars	2	W			Sommaire du Match
142 - 2021-03-12	938	Jayhawks	3	Chiefs	4	L	X		Sommaire du Match
144 - 2021-03-14	954	Thunder	2	Jayhawks	3	W	X	X	Sommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17	979	Cabaret Lady Mary Ann	2	Jayhawks	5	W			Sommaire du Match
151 - 2021-03-21	1006	Crunch	5	Jayhawks	4	L	X	X	Sommaire du Match
155 - 2021-03-25	1032	Jayhawks	3	Comets	7	L			Sommaire du Match
157 - 2021-03-27	1046	Jayhawks	6	Heat	5	W			Sommaire du Match
160 - 2021-03-30	1067	Jayhawks	1	Oceanics	3	L			Sommaire du Match
163 - 2021-04-02	1092	Comets	4	Jayhawks	2	L			Sommaire du Match
165 - 2021-04-04	1107	Wolf Pack	1	Jayhawks	4	W			Sommaire du Match
167 - 2021-04-06	1122	Stars	4	Jayhawks	3	L			Sommaire du Match
169 - 2021-04-08	1135	Las Vegas	1	Jayhawks	2	W			Sommaire du Match
171 - 2021-04-10	1152	Cougars	2	Jayhawks	3	W			Sommaire du Match
173 - 2021-04-12	1169	Jayhawks	4	Monarchs	2	W			Sommaire du Match
176 - 2021-04-15	1190	Jayhawks	1	Las Vegas	3	L			Sommaire du Match
179 - 2021-04-18	1214	Chill	4	Jayhawks	3	L	X	X	Sommaire du Match
180 - 2021-04-19	1223	Jayhawks	-	Sharks	-
182 - 2021-04-21	1235	Jayhawks	-	Monsters	-
184 - 2021-04-23	1253	Comets	-	Jayhawks	-
186 - 2021-04-25	1259	Oceanics	-	Jayhawks	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	50	20
Assistance	47,373	25,575
Assistance PCT	60.73%	65.58%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
2	1870 - 62.35%	73,850$	2,880,150$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
2,146,491$	1,714,917$	1,714,917$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
9,220$	2,146,491$	21	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
147,700$	7	9,220$	64,540$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT